Ovarian cancer is one of the most lethal gynecologic malignancies, largely due to its late diagnosis and heterogeneous nature. Understanding the molecular subtypes of ovarian cancer is critical for personalized treatment and improving patient outcomes. Traditional biopsy methods, while informative, are invasive and sometimes not feasible preoperatively. Advanced imaging techniques combined with machine learning offer a promising non-invasive alternative for molecular subtype classification. In this context, the use of Multi-Sequence Feature Fusion Network-Based Multi-Parametric Apparent Magnetic Resonance Imaging (AMRI) presents a groundbreaking approach.
Published Date: 2024-03-29; Received Date: 2024-03-02
